Projection Theorems for Harmonic Measure in NTA Domains

Suppose D is an NTA domain, E D is any closed set, and P ^x ₀(E) is the projection with respect to a point x ₀D of the set E onto the boundary of D. The projection P ^x ₀ satisfies certain geometric properties so that it is a generalization of the notion of radial projection with respect to a point x ₀ onto a boundary of a domain. It is shown that the harmonic measure of E with respect to the domain DE evaluated at the point x ₀ is bounded below by a constant times the harmonic measure of the set P ^x ₀(E) with respect to the domain D evaluated at the point x ₀. The constant is independent of the set E but it may depend upon x ₀.     

The Bifurcation of the Equatorial Periodic Orbit of the Planar Polynomial Vector Fields

In this paper, we have obtained the necessary and sufficient condition for the set of points at infinity on the plane R ² to be a periodic orbit which is called an equatorial periodic orbit of a planar vector field X(x), and the formulae about the multiplicity of the equatorial periodic orbit of X(x). We have also proved that the main result of [9] is erroneous with regard to the formulae. This work is supported by the NSF of China     

The Jump of the Laplacian on a Submanifold

Assume that a submanifold M ? ?ⁿ of an arbitrary codimension k ? {1, …, n} is closed in some open set O→?ⁿ. With a given function u ? C²(O\M) we may associate its trivial extension u: O→? such that u|_O\M=u and u|m ≡ 0. The jump of the Laplacian of the function u on the submanifold M is defined by the distribution Δu — Δu. By applying some general version of the Fubini theorem to the nonlinear projection onto M we obtain the formula for the jump of the Laplacian (Theorem 2.2).     

Efficient Computation of the Hausdorff Distance Between Polytopes by Exterior Random Covering

In this paper, a simple yet efficient randomized algorithm (Exterior Random Covering) for finding the maximum distance from a point set to an arbitrary compact set in R^d is presented. This algorithm can be used for accelerating the computation of the Hausdorff distance between complex polytopes.     

Global Convergence of a Modified Gradient Projection Method for Convex Constrained Problems

In this paper, the continuously differentiable optimization problem min{f（x） ： x∈Ω}, where Ω ∈ R^n is a nonempty closed convex set, the gradient projection method by Calamai and More （Math. Programming, Vol.39. P.93-116, 1987） is modified by memory gradient to improve the convergence rate of the gradient projection method is considered. The convergence of the new method is analyzed without assuming that the iteration sequence {x^k} of bounded. Moreover, it is shown that, when f（x） is pseudo-convex （quasiconvex） function, this new method has strong convergence results. The numerical results show that the method in this paper is more effective than the gradient projection method.     

On the index of Fredholm pairs of idempotents

Using the technique of block-operators, in this note, we prove that if P and Q are idempotents and （P - Q）^2n＋1 is in the trace class, then （P - Q）^2m＋1 is also in the trace class and tr（P - Q）^2m＋1 = dim（k（P） ∩ k（Q）^⊥） -dim（k（P）^⊥ N k（Q））, for all m ≥ n. Moreover, we prove that dim（k（P）∩ k（Q）^⊥） = dim（k（P）^⊥ ∩k（Q）） if and only if there exists a unitary U such that UP = QU and PU = UQ, where k（T） denotes the range of T. Keywords Fredholm, orthogonal projection, positive operator     

When Is a Sum of Partial Reflections Equal to a Scalar Operator?

We describe the set of values of the parameter for which there exists a Hilbert space H and n partial reflections A ₁,...,A _n (self-adjoint operators such that A _k ³ =A_k or, which is the same, self-adjoint operators whose spectra belong to the set {-1,0,1}) whose sum is equal to the scalar operator I _H .     

Metric Projections and Polyhedral Spaces

We prove that if X is a Banach space and Y is a proximinal subspace of finite codimension in X such that the finite dimensional annihilator of Y is polyhedral, then the metric projection from X onto Y is lower Hausdorff semi continuous. In particular this implies that if X and Y are as above, with the unit sphere of the annihilator space of Y contained in the set of quasi-polyhedral points of X ^*, then the metric projection onto Y is Hausdorff metric continuous. Partially supported under project DST/INT/US-NSF/RPO/141/2003.     

Representation of Reproducing Kernels and the Lebesgue Constants on the Ball

For the weight function (1−x²)^μ−1/2 on the unit ball, a closed formula of the reproducing kernel is modified to include the case −1/2<μ<0. The new formula is used to study the orthogonal projection of the weighted L² space onto the space of polynomials of degree at most n, and it is proved that the uniform norm of the projection operator has the growth rate of n^(d−1)/2 for μ<0, which is the smallest possible growth rate among all projections, while the rate for μ0 is n^μ+(d−1)/2.     

Bounds for Relative Projection Constants in L 1 (-1,1)

For n -dimensional subspaces E _n , F _n of L ¹ (-1,1) with E _n spanned by Chebyshev polynomials of the second kind and F _n the set of Müntz polynomials with , , it is shown that the relative projection constants satisfy (E _n , L ¹ (-1,1)) C log n and (F _n , L ¹ (-1,1)) = O(1) , . The spaces L ¹ _w(α,β) , where w _α,β is the weight function of the Jacobi polynomials and , are also studied. The Jacobi partial sum projections, which are used in connection with E _n , are not minimal. September 26, 1996.     

Minimal projections in spaces of functions of N variables

We will construct a minimal and co-minimal projection from L^p([0,1]ⁿ) onto L^p([0,1]^n₁)++L^p([0,1]^n_k), where n=n₁++n_k (see Theorem 2.9). This is a generalization of a result of Cheney, Halton and Light from (Approximation Theory in Tensor Product Spaces, Lecture Notes in Mathematics, Springer, Berlin, 1985; Math. Proc. Cambridge Philos. Soc. 97 (1985) 127; Math. Z. 191 (1986) 633) where they proved the minimality in the case n=2. We provide also some further generalizations (see Theorems 2.10 and 2.11 (Orlicz spaces) and Theorem 2.8). Also a discrete case (Theorem 2.2) is considered. Our approach differs from methods used in [8,13,20].     

Successive projection method for solving the unbalanced Procrustes problem

We present a successive projection method for solving the unbalanced Procrustes problem: given matrix A∈Rn×n and B∈Rn×k, n>k, minimize the residual‖AQ-B‖F with the orthonormal constraint QTQ = Ik on the variant Q∈Rn×k. The presented algorithm consists of solving k least squares problems with quadratic constraints and an expanded balance problem at each sweep. We give a detailed convergence analysis. Numerical experiments reported in this paper show that our new algorithm is superior to other existing methods.     

Iterative Averaging of Entropic Projections for Solving Stochastic Convex Feasibility Problems

The problem considered in this paper is that of finding a point which iscommon to almost all the members of a measurable family of closed convexsubsets of R₊₊ ⁿ , provided that such a point exists.The main results show that this problem can be solved by an iterative methodessentially based on averaging at each step the Bregman projections withrespect to f(x)=_i=1 ⁿx_i· ln x_i ofthe current iterate onto the given sets.     

On the gradient-projection method for solving the nonsymmetric linear complementarity problem

The Levitin-Poljak gradient-projection method is applied to solve the linear complementarity problem with a nonsymmetric matrixM, which is either a positive-semidefinite matrix or aP-matrix. Further-more, if the quadratic functionx ^T(Mx + q) is pseudoconvex on the feasible region {x R ⁿ |Mx + q 0,x0}, then the gradient-projection method generates a sequence converging to a solution, provided that the problem has a solution. For the case when the matrixM is aP-matrix and the solution is nondegenerate, the gradient-projection method is finite.This work is based on the author's PhD Dissertation, which was supported by NSF Grant No. MCS-79-01066 at the University of Wisconsin, Madison, Wisconsin.The author would like to thank Professor O. L. Mangasarian for his guidance of the dissertation.     

Minimal Multi-Convex Projections Onto Subspaces of Incomplete Algebraic Polynomials

Let X = (C ^N [0, 1], ‖·‖), where N ≥ 3 and let V be a linear subspace of Π _N , where Π _N denotes the space of algebraic polynomials of degree less than or equal to N. Denote by 𝒫 _S = 𝒫 _S (X, V) = {P: X → V | P-linear and bounded P| _V  = id _V , PS ? S}, where S denotes a cone of multi-convex functions. In [25 G. Lewicki and M. Prophet ( 2006 ). Minimal shape-preserving projections onto Π _n : generalizations and extensions . Numer. Func. Anal. Optim. 27 ( 7–8 ): 847 – 873 .[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar], 26 G. Lewicki and M. Prophet ( 2007 ). Minimal multi-convex projections . Studia Mathematica 178 ( 2 ): 99 – 124 . [Google Scholar]], the multi-convex projections were defined and it was shown the explicite formula for projection with minimal norm in 𝒫 _S for V = Π _N . In this article we present a generalization of these results in the case of V being certain, proper subspaces of Π _N .     

An infeasible-interior-point algorithm using projections onto a convex set

We present a new class of primal-dual infeasible-interior-point methods for solving linear programs. Unlike other infeasible-interior-point algorithms, the iterates generated by our methods lie in general position in the positive orthant of ² and are not restricted to some linear manifold. Our methods comprise the following features: At each step, a projection is used to recenter the variables to the domainx _i s _i . The projections are separable into two-dimensional orthogonal projections on a convex set, and thus they are seasy to implement. The use of orthogonal projections allows that a full Newton step can be taken at each iteration, even if the result violates the nonnegativity condition. We prove that a short step version of our method converges in polynomial time.Research performed while visiting the Institut für Angewandte Mathematik, University of Würzburg, D-87074 Würzburg, Germany, as a Research Fellow of the Alexander von Humboldt Foundation.     

Convergence to equilibrium for linear spatially homogeneous Boltzmann equation with hard and soft potentials: A semigroup approach in L1‐spaces

We investigate the large time behavior of solutions to the spatially homogeneous linear Boltzmann equation from a semigroup viewpoint. Our analysis is performed in some (weighted) L¹‐spaces. We deal with both the cases of hard and soft potentials (with angular cut‐off). For hard potentials, we provide a new proof of the fact that, in weighted L¹‐spaces with exponential or algebraic weights, the solutions converge exponentially fast towards equilibrium. Our approach uses weak‐compactness arguments combined with recent results of the second author on positive semigroups in L¹‐spaces. For soft potentials, in L¹‐spaces, we exploit the convergence to ergodic projection for perturbed substochastic semigroup to show that, for very general initial datum, solutions to the linear Boltzmann equation converges to equilibrium in large time. Moreover, for a large class of initial data, we also prove that the convergence rate is at least algebraic. Notice that, for soft potentials, no exponential rate of convergence is expected because of the absence of spectral gap.     

Mixed projection methods for systems of variational inequalities

Let H be a real Hilbert space. Let be bounded and continuous mappings where D(F) and D(K) are closed convex subsets of H. We introduce and consider the following system of variational inequalities: find [u ^*,v ^*]∈D(F) × D(K) such that This system of variational inequalities is closely related to a pseudomonotone variational inequality. The well-known projection method is extended to develop a mixed projection method for solving this system of variational inequalities. No invertibility assumption is imposed on F and K. The operators K and F also need not be defined on compact subsets of H.      

On the spectrum of critical sets in latin squares of order 2n

Suppose that L is a latin square of order m and P ? L is a partial latin square. If L is the only latin square of order m which contains P, and no proper subset of P has this property, then P is a critical set of L. The critical set spectrum problem is to determine, for a given m, the set of integers t for which there exists a latin square of order m with a critical set of size t. We outline a partial solution to the critical set spectrum problem for latin squares of order 2ⁿ. The back circulant latin square of even order m has a well‐known critical set of size m²/4, and this is the smallest known critical set for a latin square of order m. The abelian 2‐group of order 2ⁿ has a critical set of size 4ⁿ‐3ⁿ, and this is the largest known critical set for a latin square of order 2ⁿ. We construct a set of latin squares with associated critical sets which are intermediate between the back circulant latin square of order 2ⁿ and the abelian 2‐group of order 2ⁿ. © 2007 Wiley Periodicals, Inc. J Combin Designs 16: 25–43, 2008